Artificial neural network models have been developed to predict rotation velocity and ion temperature profiles on the EAST tokamak based on spectral measurements from the x-ray crystal spectrometer. Both Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models have been employed to infer line-integrated ion temperatures. The predicted results from these two models exhibit a strong correlation with the target values, providing an opportunity for cross-validation to enhance prediction accuracy. Notably, the computational speed of these models has been significantly increased, surpassing traditional methods by over tenfold. Furthermore, the investigation of input data range and error prediction serves as the foundation for future automated calculation process. Finally, CNNs have also been employed to predict line-integrated rotation velocity profiles and inverted ion temperature profiles for their robustness in the training process. It is noted that these algorithms are not restricted to any specific physics model and can be readily adapted to various fusion devices.
This paper describes the development of artificial neural network models to predict plasma rotation velocity and ion temperature profiles in the EAST Tokamak. The models, including Deep Neural Network (DNN) and Convolutional Neural Network (CNN), can infer these profiles from spectral measurements, providing a faster and more accurate alternative to traditional methods. The study also explores the input data range and error prediction to improve the automated calculation process.